Fixed docs build issues

Author: C-Achard

docs/index.rst

@@ -24,6 +24,7 @@ Welcome to napari-cellseg3d's documentation!
     :maxdepth: 2
     :caption: Advanced guides and walk-through:
 
+    res/guides/training_wnet
     res/guides/detailed_walkthrough
     res/guides/custom_model_template
 

docs/res/code/instance_segmentation.rst

@@ -44,10 +44,6 @@ clear_small_objects
 **************************************
 .. autofunction:: napari_cellseg3d.code_models.instance_segmentation::clear_small_objects
 
-to_instance
-**************************************
-.. autofunction:: napari_cellseg3d.code_models.instance_segmentation::to_instance
-
 to_semantic
 **************************************
 .. autofunction:: napari_cellseg3d.code_models.instance_segmentation::to_semantic

docs/res/guides/detailed_walkthrough.rst

@@ -1,7 +1,7 @@
 .. _detailed_walkthrough:
 
 Detailed walkthrough - Supervised learning
-===================================
+===================================================
 
 The following guide will show you how to use the plugin's workflow, starting from human-labeled annotation volume, to running inference on novel volumes.
 

docs/res/guides/inference_module_guide.rst

@@ -132,13 +132,17 @@ WNet
 The WNet model, from the paper `WNet, A Deep Model for Fully Unsupervised Image Segmentation`_, is a fully unsupervised model that can be used to segment images without any labels.
 It clusters pixels based on brightness, and can be used to segment cells in a variety of modalities.
 Its use and available options are similar to the above models, with a few differences :
+
 .. note::
     | Our provided, pre-trained model should use an input size of 64x64x64. As such, window inference is always enabled
     | and set to 64. If you want to use a different size, you will have to train your own model using the provided notebook.
-All it requires are images; for nucleus segmentation, it is recommended to use 2 classes (default).
+
+All it requires are 3D .tif images (you can also load a 2D stack as 3D via napari).
 
 Source code
 --------------------------------
 * :doc:`../code/plugin_model_inference`
 * :doc:`../code/model_framework`
 * :doc:`../code/workers`
+
+

docs/res/guides/training_module_guide.rst

@@ -1,7 +1,14 @@
 .. _training_module_guide:
 
-Training module guide
-=================================
+Training module guide - Unsupervised models
+==============================================
+
+.. important::
+    The WNet training is for now only available in the provided jupyter notebook, in the ``notebooks`` folder.
+    Please see the :ref:`training_wnet` section for more information.
+
+Training module guide - Supervised models
+==============================================
 
 This module allows you to train pre-defined Pytorch models for cell segmentation.
 Pre-defined models are stored in napari-cellseg-3d/models.

docs/res/welcome.rst

@@ -28,6 +28,7 @@ From this page you can access the guides on the several modules available for yo
     * Compute scores : :ref:`metrics_module_guide`
 
 * Advanced :
+    * Training WNet (in jupyter, support might be added for plugin later) : :ref:`training_wnet`
     * Defining custom models directly in the plugin (WIP) : :ref:`custom_model_guide`
 
 
@@ -51,8 +52,7 @@ Requirements
 .. important::
     This package requires you have napari installed with PyQt5 or PySide2 first.
     If you do not have a Qt backend you can use :
-
-        ``pip install napari-cellseg3d[all]``
+    ``pip install napari-cellseg3d[all]``
     to install PyQt5 by default.
 
 It also depends on PyTorch and some optional dependencies of MONAI. These come in the pip package as requirements, but if